class_docstr: Base class for implementing dataset builders. See more information
  on top. Child class needs to implement ``build`` and ``load``.
  
  Args:
      dataset_name (str): Name of the dataset passed from child.
docstr: This is used to prepare the dataset and load it from a path.
  Override this method in your child dataset builder class.
  
  Args:
      config (DictConfig): Configuration of this dataset loaded from config.
      dataset_type (str): Type of dataset, train|val|test
  
  Returns:
      dataset (BaseDataset): Dataset containing data to be trained on
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code: def load(self, config, dataset_type="train", *args, **kwargs):
      """
      This is used to prepare the dataset and load it from a path.
      Override this method in your child dataset builder class.
  
      Args:
          config (DictConfig): Configuration of this dataset loaded from config.
          dataset_type (str): Type of dataset, train|val|test
  
      Returns:
          dataset (BaseDataset): Dataset containing data to be trained on
      """
      raise NotImplementedError(
          "This dataset builder doesn't implement a load method"
      )
  
imports: import uuid
  from typing import Optional
  import pytorch_lightning
  from mmf.utils.build import build_dataloader_and_sampler
  from mmf.utils.logger import log_class_usage
  from omegaconf import DictConfig
  from torch.utils.data import Dataset
label_desc: Use image node_id 14 for calling the BaseDatasetBuilder obj's underlying member method code with example usage: obj.load(config, dataset_type) without return types